Instructions to use sraj/Merge_Drop_SYN_FastText with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sraj/Merge_Drop_SYN_FastText with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sraj/Merge_Drop_SYN_FastText")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sraj/Merge_Drop_SYN_FastText") model = AutoModelForMaskedLM.from_pretrained("sraj/Merge_Drop_SYN_FastText") - Notebooks
- Google Colab
- Kaggle
File size: 932 Bytes
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base_model:
- sraj/CMB_MARK_CX_LRD
- sraj/CMB_FWEdu_V2_FastTxt_CX_LRD
library_name: transformers
tags:
- mergekit
- merge
---
# merge_drop_SYN
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [sraj/CMB_MARK_CX_LRD](https://huggingface.co/sraj/CMB_MARK_CX_LRD)
* [sraj/CMB_FWEdu_V2_FastTxt_CX_LRD](https://huggingface.co/sraj/CMB_FWEdu_V2_FastTxt_CX_LRD)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: sraj/CMB_MARK_CX_LRD
parameters:
weight: 1.0
- model: sraj/CMB_FWEdu_V2_FastTxt_CX_LRD
parameters:
weight: 1.0
merge_method: linear
parameters:
normalize: true
dtype: bfloat16
```
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